A mathematical morphological approach for segmenting heavily noise-corrupted images

Image segmentation is an important pre-processing step before object recognition. Here, we propose a method based on mathematical morphology for segmenting images which are heavily corrupted with noise. First we separate the foreground speck noise, the object surface and the noisy background in the image by means of the grey-scale skeleton transformation and the concept of maximal inscribed blocks. By removing the varying background and speck noise, the image is enhanced. To successfully recognize and separate the unwanted components, a size characterization algorithm is formulated based on the grey-scale morphological opening. Finally, a global thresholding can be applied to the enhanced image to obtain the object from the background. Experimental results are included to illustrate its superiority over the other two segmentation algorithms.

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